{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic Dataset: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we're going to analyse the famous Titanic dataset from Kaggle. The dataset is meant for supervised machine learning, but we're only going to do some exploratory analysis at this stage.\n",
    "\n",
    "We'll try to answer the following questions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Who were the passengers on the Titanic? (age, gender, class.. etc)\n",
    "- What deck were the passengers on and how does that relate to their class?\n",
    "- Where did the passengers come from?\n",
    "- Who was alone and who was with family?\n",
    "- What factors helped someone survive the sinking?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The titanic data is available through Kaggle, after sign-up.\n",
    "titanic_df = pd.read_csv('data/titanic/titan_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at the first few rows in the dataset.\n",
    "titanic_df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#Information about the dataset. \n",
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above, we see that the dataset is missing a lot of information in for the __Cabin__ column. We'll need to deal with that when we go about using the cabin data. \n",
    "\n",
    "Other information seems to be complete, except some __Age__ entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas' awesome _describe_ method quickly gives us some aggregate stats for the dataset. \n",
    "\n",
    "A 1 in the Survived column means that the person survived, while a 0 mean that they died. So looking at the mean, we can say that only ~38% people survived the sinking. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Who were the passengers on the Titanic?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at some demographical information about the passengers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17d4c0130>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating a factorplot, charting the number of male and female passengers\n",
    "sns.catplot(x='Sex',data=titanic_df,kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsurprisingly, there were considerably more male passengers onboard. \n",
    "\n",
    "Let's take a look at how the passengers were divided among different classes. \n",
    "\n",
    "_Note: The __Pclass__ attribute is a proxy for the socio-economic class of a person._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10661f3a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Pclass',data=titanic_df,hue='Sex',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another thing we can do before we start infering something from the above factorplot, is to divide the passengers between male, female, and a child."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to detect if a person is a man, woman or child.\n",
    "def man_wom_chi(passenger):\n",
    "    age=passenger['Age']\n",
    "    sex=passenger['Sex']\n",
    "    \n",
    "    return 'child' if age < 16 else sex\n",
    "\n",
    "#Using Pandas' apply method to create a new column \"Person\"\n",
    "titanic_df['Person'] = titanic_df.apply(man_wom_chi,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>child</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  \n",
       "5      0            330877   8.4583   NaN        Q    male  \n",
       "6      0             17463  51.8625   E46        S    male  \n",
       "7      1            349909  21.0750   NaN        S   child  \n",
       "8      2            347742  11.1333   NaN        S  female  \n",
       "9      0            237736  30.0708   NaN        C   child  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at the first 10 rows in the dataset, to see if our method worked.\n",
    "titanic_df[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "male      537\n",
      "female    271\n",
      "child      83\n",
      "Name: Person, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Getting the actual counts\n",
    "print(titanic_df['Person'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's create a factorplot to check out the distribution of men, women and children belonging to different classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17e05e320>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P1bQOex+piYmJWrx4cesypI3O6z+89vOL+3HD6Uevf6fZaabOWzRjeZtOktScYSRJas4wkiQ1ZxhJkpozjCRJzRlGkqTm/J2RJI3IfmfuN62/pbnmhGue8xDyJOcCX6uqL63V/jLgjKo6PMkBwIlV9Ru/Q0pyFzBRVfc/n5qfjWEkSaKq/h44vNX1vU0nSbNYkqOT3JLk5iRf6Jr3T/L9JHckObzbb16SW6c4/qVJ/jbJsiT/k55+0GsYSdIslWQP4KPAgd3UDx/oNs0F3szgcUCfXM9pPgZ8r6r2AL7CM0/6nlbeppOk2etA4OI13+90k+QBfLWqngZuS7L9es6xP4O5jqiqryd5qI9C7RlJ0viZPGXEjHiOnmEkSbPXlcARSV4KkGTb53GOq4E/7o4/GNhm+sp7hrfpJGlEns9Q7A1RVcuSnAZ8J8lq4KbncZqPAxckWQZ8H7h7OmtcwzCSpFmsqhYBi9axfcvu/S5gz27528C3u+UHGExZ3itv00mSmjOMJEnNjcVtOme1lKSZzZ6RJKk5w0iS1JxhJElqbiy+M5LUxt2nvmYk19n55KUjuY76Y89IktScYSRJas4wkiQ1ZxhJkpozjCRJzRlGkqTmDCNJUnOGkSSpOcNIktRcb2GU5Jwk9yW5dVLbtkkuT/KT7n2brj1JzkiyPMktSV7XV12SpJmnz57RucBBa7WdBFxRVbsBV3TrAAcDu3WvBcBZPdYlSZphegujqroaeHCt5kN5ZvrbRcBhk9rPq4Frga2TzO2rNknSzDLq74y2r6qV3fLPge275R2An03a756u7TckWZBkcZLFq1at6q9SSdLINHtqd1VVknoexy0EFgJMTEw85+PVzqhm3AVn3ZU2NqPuGd275vZb935f174C2GnSfjt2bZKkMTDqMLoMOKZbPga4dFL70d2oun2BRybdzpMkzXK93aZLcgFwALBdknuAjwGfBC5KchzwU+Ad3e7fAA4BlgOPA+/uqy5J0szTWxhV1ZHPsuktU+xbwPv6qkWSNLP5BAZJUnOGkSSpuWZDu2eju099zUius/PJS0dyHUkaFXtGkqTmDCNJUnOGkSSpOcNIktScYSRJas4wkiQ1ZxhJkpozjCRJzRlGkqTmDCNJUnOGkSSpOcNIktScYSRJas4wkiQ1ZxhJkpozjCRJzRlGkqTmDCNJUnOGkSSpOcNIktScYSRJas4wkiQ1t2nrAqQ+3H3qa0ZynZ1PXjqS60iznT0jSVJzhpEkqTnDSJLUnGEkSWrOMJIkNWcYSZKaM4wkSc35OyNJG739ztxvJNe55oRrRnKdcWTPSJLUnGEkSWrOMJIkNdfkO6MkdwGPAquBp6pqIsm2wN8A84C7gHdU1UMt6pMkjVbLntHvVtX8qpro1k8Crqiq3YArunVJ0hiYSbfpDgUWdcuLgMPalSJJGqVWYVTA3ya5IcmCrm37qlrZLf8c2H6qA5MsSLI4yeJVq1aNolZJUs9a/c7ozVW1Isk/By5P8qPJG6uqktRUB1bVQmAhwMTExJT7SJI2Lk16RlW1onu/D/gKsA9wb5K5AN37fS1qkySN3sjDKMkWSbZaswz8PnArcBlwTLfbMcClo65NktRGi9t02wNfSbLm+l+sqm8m+SFwUZLjgJ8C72hQmySpgZGHUVXdAew1RfsDwFtGXY8kqb2ZNLRbkjSmDCNJUnOGkSSpOcNIktScYSRJas4wkiQ1ZxhJkpozjCRJzRlGkqTmDCNJUnOGkSSpOcNIktScYSRJas4wkiQ1ZxhJkpozjCRJzRlGkqTmDCNJUnOGkSSpOcNIktScYSRJas4wkiQ1t2nrAqSN2X5n7jeS61xzwjUjuY7Uij0jSVJzhpEkqTnDSJLUnGEkSWrOMJIkNWcYSZKaM4wkSc0ZRpKk5gwjSVJzhpEkqTnDSJLUnM+m2wiN6nlo4DPRJI2GPSNJUnOGkSSpuRkXRkkOSvLjJMuTnNS6HklS/2ZUGCXZBPhr4GBgd+DIJLu3rUqS1LcZFUbAPsDyqrqjqn4JXAgc2rgmSVLPUlWta/i1JIcDB1XVf+jWjwLeWFXvn7TPAmBBt/oq4McjL3R6bQfc37oIAf4tZorZ8He4v6oOal3ExmSjG9pdVQuBha3rmC5JFlfVROs65N9ipvDvMJ5m2m26FcBOk9Z37NokSbPYTAujHwK7JdklyebAO4HLGtckSerZjLpNV1VPJXk/8C1gE+CcqlrWuKy+zZpbjrOAf4uZwb/DGJpRAxgkSeNppt2mkySNIcNIktScYdRIknOS3Jfk1ta1jLMkOyW5KsltSZYl+UDrmsZVkhcluT7Jzd3f4uOta9Lo+J1RI0n2Bx4DzquqPVvXM66SzAXmVtWNSbYCbgAOq6rbGpc2dpIE2KKqHkuyGfA94ANVdW3j0jQC9owaqaqrgQdb1zHuqmplVd3YLT8K3A7s0Laq8VQDj3Wrm3Uv/1seE4aR1EkyD9gbuK5xKWMrySZJlgD3AZdXlX+LMWEYSUCSLYFLgA9W1S9a1zOuqmp1Vc1n8PSVfZJ4C3tMGEYae933E5cA51fVl1vXI6iqh4GrAB82OiYMI4217kvzs4Hbq+qzresZZ0nmJNm6W34x8FbgR02L0sgYRo0kuQD4AfCqJPckOa51TWNqP+Ao4MAkS7rXIa2LGlNzgauS3MLgOZWXV9XXGtekEXFotySpOXtGkqTmDCNJUnOGkSSpOcNIktScYSRJas4w0qySZHU3PPvWJBcn+a117HtKkhNHWZ+kqRlGmm2eqKr53ZPQfwm8t3VBktbPMNJs9l3gFQBJjk5ySzdXzhfW3jHJ8Ul+2G2/ZE2PKskRXS/r5iRXd217dPPuLOnOudtIP5U0C/mjV80qSR6rqi2TbMrgeXPfBK4GvgK8qaruT7JtVT2Y5BTgsar6dJKXVtUD3Tk+AdxbVWcmWQocVFUrkmxdVQ8nORO4tqrOT7I5sElVPdHkA0uzhD0jzTYv7qYgWAzczeC5cwcCF1fV/QBVNdU8Unsm+W4XPu8C9ujarwHOTXI8sEnX9gPgI0n+BPhtg0jacJu2LkCaZk90UxD82uBZqOt1LoMZXm9OcixwAEBVvTfJG4E/BG5I8vqq+mKS67q2byR5T1VdOX0fQRo/9ow0Dq4EjkjyUoAk206xz1bAym46iXetaUyya1VdV1UnA6uAnZK8HLijqs4ALgVe2/snkGY5e0aa9apqWZLTgO8kWQ3cBBy71m5/xmCG11Xd+1Zd++ndAIUAVwA3A38CHJXkV8DPgb/o/UNIs5wDGCRJzXmbTpLUnGEkSWrOMJIkNWcYSZKaM4wkSc0ZRpKk5gwjSVJz/x/AAlbob1j9UQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Pclass',data=titanic_df,hue='Person',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's interesting to note above, is that there are way more male passengers than woman or childeren, in the 3rd class, than any other class. This will lead to an interesting analysis later on.\n",
    "\n",
    "We can quickly create a histogram from the dataset, to check out the distribution of passengers of different age groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_df['Age'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check out how the age distribution varies for different classes. We'll use Seaborn's [Facetgrid](https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.FacetGrid.html) for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17e08b6a0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 906.375x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df,hue='Pclass',aspect=4)\n",
    "\n",
    "fig.map(sns.kdeplot,'Age',shade=True)\n",
    "\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "fig.set(xlim=(0,oldest))\n",
    "\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quite clearly, we can see that the first class passengers belonged more to the middle age groups, with very few children.\n",
    "\n",
    "Second and third class had more children, with relatively fewer older people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17e187790>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 934.5x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df,hue='Sex',aspect=4)\n",
    "\n",
    "fig.map(sns.kdeplot,'Age',shade=True)\n",
    "\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "fig.set(xlim=(0,oldest))\n",
    "\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The age distribution seems to be less varied in terms of male and female passengers, though we can say that there more younger females than younger males."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What deck were the passengers on and how does that relate to their class?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we're doing open ended analysis, let's try and check out if people belonging to higher classes were assigned cabins on a higher deck (or level) of the ship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Because the cabin data was missing in a lot of cases, we can just drop it for now for this section.\n",
    "deck_df = titanic_df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>child</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "1             2         1       1   \n",
       "3             4         1       1   \n",
       "6             7         0       1   \n",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "\n",
       "    Parch    Ticket     Fare Cabin Embarked  Person  \n",
       "1       0  PC 17599  71.2833   C85        C  female  \n",
       "3       0    113803  53.1000  C123        S  female  \n",
       "6       0     17463  51.8625   E46        S    male  \n",
       "10      1   PP 9549  16.7000    G6        S   child  \n",
       "11      0    113783  26.5500  C103        S  female  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we find out what deck a passenger was assigned? \n",
    "\n",
    "The __Cabin__ attribtute holds that data. Intuitively, the cabin number of a passenger is a combination of the deck they're on, and their room number. So a passenger on deck 'C' will have a cabin number in _CXXX_ format, where XXX can be a room number.\n",
    "\n",
    "We just need to create a python method to extract first character from the cabin information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grabbing the deck from the cabin numbers\n",
    "def get_level(passenger):\n",
    "    cabin = passenger['Cabin']\n",
    "    return cabin[0]\n",
    "\n",
    "# get_level[deck_df.iloc[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_1/tfp0k5355jxb501q_ckbc6m80000gn/T/ipykernel_68366/4030337562.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  deck_df['level']=deck_df.apply(get_level,axis=1)\n"
     ]
    }
   ],
   "source": [
    "deck_df['level']=deck_df.apply(get_level,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Person</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>child</td>\n",
       "      <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "1             2         1       1   \n",
       "3             4         1       1   \n",
       "6             7         0       1   \n",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "\n",
       "    Parch    Ticket     Fare Cabin Embarked  Person level  \n",
       "1       0  PC 17599  71.2833   C85        C  female     C  \n",
       "3       0    113803  53.1000  C123        S  female     C  \n",
       "6       0     17463  51.8625   E46        S    male     E  \n",
       "10      1   PP 9549  16.7000    G6        S   child     G  \n",
       "11      0    113783  26.5500  C103        S  female     C  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything seems to work, so now we can check out how many passengers belonged to different decks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17de299c0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='level',data=deck_df,palette='winter_d',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And to answer our original question.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17e280820>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='level',data=deck_df,hue='Pclass',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It feels like passengers belonging to the upper classes are indeed assigned to the upper decks. Decks A,B and C were assigned only to first class passengers, and we start seeing third class passengers only from level E. Though, we have to keep in mind that cabin information was missing for more than 3/4 of our passengers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Where did the passengers come from?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The __Embarked__ attribute contains data for the passengers' port of embarkation (C = Cherbourg; Q = Queenstown; S = Southampton). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17de28f10>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Embarked',data=titanic_df,hue='Pclass',kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the passengers embarked from Southampton (including Jack and Rose, in the movie). What's also interesting, is that almost no first or second class passengers embarked from Queenstown, which can tell us something about the socio-economic status of the population of Queenstown."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Who was with their family?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take another look at our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The __SibsSP__ attribute refers to the number of siblings or spouses that a passenger had aboard. __Parch__ refers to the number of parents or children someone had on the ship.\n",
    "\n",
    "As we're just trying to know if a passenger had _someone_ from his family onboard, we can make our life a bit easier by making another column to represent this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding the number of family a passenger had onboard\n",
    "titanic_df['Alone'] = titanic_df.SibSp + titanic_df.Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
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       "      <td>female</td>\n",
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       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
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       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
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       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  Person  \\\n",
       "886    male  27.0      0      0      211536  13.00   NaN        S    male   \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  female   \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  female   \n",
       "889    male  26.0      0      0      111369  30.00  C148        C    male   \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q    male   \n",
       "\n",
       "     Alone  \n",
       "886      0  \n",
       "887      0  \n",
       "888      3  \n",
       "889      0  \n",
       "890      0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anything above the other than 0 in the new __Alone__ column means that the person wasn't alone. So we're going to use that column to define our 'Alone' stats more clearly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sajal/.pyenv/versions/3.10.0/envs/ds_portfolio/lib/python3.10/site-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_block(indexer, value, name)\n"
     ]
    }
   ],
   "source": [
    "titanic_df['Alone'].loc[titanic_df['Alone']>0] = 'No'\n",
    "\n",
    "titanic_df['Alone'].loc[titanic_df['Alone']==0] = 'Yes'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Person Alone  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male    No  \n",
       "1      0          PC 17599  71.2833   C85        C  female    No  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female   Yes  \n",
       "3      0            113803  53.1000  C123        S  female    No  \n",
       "4      0            373450   8.0500   NaN        S    male   Yes  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x293d87310>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Alone',data=titanic_df,kind='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The information is pretty simple to digest now. The majority of people on the ship had family on the ship. But did that make a difference in their chances of survival? We'll answer the question in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What factors helped someone survive the sinking?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x293e30550>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Pclass',y='Survived',data=titanic_df,kind='point')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To not much surprise, it seems like lower class passengers had a lower chance of survival. \n",
    "\n",
    "But wait, we saw earlier that there were more men than woman or children, in the 3rd class than in any other class. From the movie 'Titanic', you might remember the 'Women and children first!' protocol that the ship's administration followed while putting passengers on the lifeboats. \n",
    "\n",
    "Let's see if this is really the case, by using the 'Person' column as the hue in the factorplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x293fe70a0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Pclass',y='Survived',hue='Person',data=titanic_df, kind='point')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this confirms our suspicion. Men had lower chances of survival than women and children, as they had lower priority of being saved. \n",
    "\n",
    "We can still generally say that men, women and children belonging to the 3rd class had a lesser chance at survival than their counterparts belonging to the 1st class.\n",
    "\n",
    "Let's see if there's a relation between the age and survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x293e84d30>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Age',y='Survived',data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x294191b10>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Age',y='Survived',hue='Pclass',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The general concensus from the above two figures seems to be that older people were less likely to survive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2941dbac0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Survived',y='Alone',data=titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if you knew someone on the ship, you had a higher chance of survival, than if you were all alone. Company's always good.\n",
    "\n",
    "To add to this project, we can try running ML algorithms on the data (both the original dataset, and with the modifications we've made), to see if we can create a model that accurately predicts the survival of a passenger. \n",
    "\n",
    "This notebook will be updated with those sections in the future."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
